Development of a video encryption algorithm for critical areas using 2D extended Schaffer function map and neural networks.

This paper proposes an encryption algorithm for crucial areas of a video based on chaos and a neural network, which SVEA (Selective Video Encryption Algorithm). The critical areas of each frame in a video are extracted by deep learning to the encryption system. A one-step encryption algorithm is used to encrypt these critical areas based on chaos, where scrambling and diffusion are simultaneously performed. A new chaotic system 2D extended Schaffer function map (2D-ESFM) is utilized in the encryption system, inspired by the Schaffer function. The system has demonstrated excellent performance through Lyapunov exponents (LEs), permutation entropy (PE), the 0-1 test, and other methods. Additionally, to resist chosen plaintext attacks, the secret key is generated by a neural network, with the critical areas of the video as inputs to the neural network. The chaotic system generates the biases and weights for the neural network. We evaluate SVEA on our dataset (Gymnastics at the Olympic Games) and public datasets. SVEA exhibits strong security characteristics compared to state-of-the-art algorithms and reduces time complexity by approximately 51.3%. • An encryption algorithm for critical areas of a video is proposed. • A 2D extended Schaffer function map (2D-ESFM) with a larger parameter space and a better performance is proposed. • The secret key of the cryptosystem is generated by the neural network, and a one-step encryption method is proposed. • A dataset is built about video encryption, called "Gymnastics at the Olympic Games". [ABSTRACT FROM AUTHOR]